Added new logic; bug fixes

app.py

@@ -26,8 +26,7 @@ from modules.auth import validate_login
 from modules.utils import create_excel, clean_text, extract_predicted_labels, predict_category, process_data
 
 # Local
-# from dotenv import load_dotenv
-# load_dotenv()
+/
 
 
 # Main app logic
@@ -80,7 +79,7 @@ def main():
             sens_options = {
                 "Low": 4,
                 "Medium": 5,
-                "High": 7,
+                "High": 6,
             }
 
             sens_input = st.sidebar.radio(label = 'Select the Sensitivity Level [OPTIONAL]',
@@ -90,7 +89,8 @@ def main():
                                     FNs at the lowest setting is approximately 6 percent, and \
                                     approaches 13 percent at the highest setting. \
                                     NOTE: changing this setting does not affect the raw data in the CSV output file (only the REVIEW/REJECT labels)', 
-                                    options = list(sens_options.keys()), 
+                                    options = list(sens_options.keys()),
+                                    index = list(sens_options.keys()).index("High"),
                                     horizontal = False)
 
             sens_level = sens_options[sens_input]
@@ -169,7 +169,7 @@ def main():
                 st.rerun()
 
 
-    # Comment out or remove the else block containing login form
+    # Comment out for testing
     else:
         username = st.text_input("Username")
         password = st.text_input("Password", type="password")

modules/utils.py

@@ -215,6 +215,16 @@ def process_data(uploaded_file, sens_level):
     # Further data processing and actions
     sector_classes = ['Energy','Transport','Industries']
     df['pred_score'] = df.apply(lambda x: round((x['fin_lab2']*2 + x['scope_lab1']*2 + x['scope_lab2']*2 + x['tech_lab1'] + x['tech_lab3']+ x['lev_gt_0']+x['lev_maf_scale'])/10*10,0), axis=1)
-    df['pred_action'] = df.apply(lambda x: 'REJECT' if (x['pred_score'] <4 or x['LANG'] != 'en-US' or x['ADAPMIT'] == 'Adaptation' or not ((x['SECTOR1'] in sector_classes) or (x['SECTOR2'] in sector_classes))) else 'REVIEW', axis=1)
+    # df['pred_action'] = df.apply(lambda x: 'REJECT' if (x['pred_score'] <4 or x['LANG'] != 'en-US' or x['ADAPMIT'] == 'Adaptation' or not ((x['SECTOR1'] in sector_classes) or (x['SECTOR2'] in sector_classes))) else 'REVIEW', axis=1)
+    df['pred_action'] = df.apply(lambda x: 
+        'INELIGIBLE' if (x['LANG'] != 'en-US' or 
+                        x['ADAPMIT'] == 'Adaptation' or 
+                        not any(sector in [x['SECTOR1'], x['SECTOR2']] for sector in sector_classes))
+        else 'REJECT' if x['pred_score'] <= sens_level
+        else 'PRE-ASSESSMENT' if sens_level+1 <= x['pred_score'] <= sens_level+2 
+        else 'FULL-ASSESSMENT' if x['pred_score'] > sens_level+2
+        else 'ERROR', axis=1)
+
+    return df
+
 
-    return df